mirror of
https://github.com/NicolasBohn/NexQuant.git
synced 2026-07-28 16:07:46 +00:00
Xu Yang
118 lines
4.5 KiB
Python
118 lines
4.5 KiB
Python
from __future__ import annotations
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from abc import abstractmethod
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from pathlib import Path
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from rdagent.components.coder.CoSTEER.config import CoSTEERSettings
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from rdagent.components.coder.CoSTEER.evolvable_subjects import EvolvingItem
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from rdagent.components.coder.CoSTEER.knowledge_management import (
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CoSTEERQueriedKnowledge,
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)
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from rdagent.components.coder.CoSTEER.scheduler import random_select
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from rdagent.core.conf import RD_AGENT_SETTINGS
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from rdagent.core.evolving_framework import EvolvingStrategy, QueriedKnowledge
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from rdagent.core.experiment import FBWorkspace, Task
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from rdagent.core.prompts import Prompts
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from rdagent.core.scenario import Scenario
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from rdagent.core.utils import multiprocessing_wrapper
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implement_prompts = Prompts(file_path=Path(__file__).parent / "prompts.yaml")
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class MultiProcessEvolvingStrategy(EvolvingStrategy):
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def __init__(self, scen: Scenario, settings: CoSTEERSettings):
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super().__init__(scen)
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self.settings = settings
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@abstractmethod
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def implement_one_task(
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self,
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target_task: Task,
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queried_knowledge: QueriedKnowledge = None,
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workspace: FBWorkspace | None = None,
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) -> dict[str, str]: # FIXME: fix interface of previous implement
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"""
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This method will input the task & current workspace,
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and output the modification to applied to the workspace.
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(i.e. replace the content <filename> with <content>)
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Return
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------
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The new files {<filename>: <content>} to update the workspace.
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"""
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raise NotImplementedError
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def select_one_round_tasks(
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self,
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to_be_finished_task_index: list,
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evo: EvolvingItem,
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selected_num: int,
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queried_knowledge: CoSTEERQueriedKnowledge,
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scen: Scenario,
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) -> list:
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"""Since scheduler is not essential, we implement a simple random selection here."""
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return random_select(to_be_finished_task_index, evo, selected_num, queried_knowledge, scen)
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@abstractmethod
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def assign_code_list_to_evo(self, code_list: list, evo: EvolvingItem) -> None:
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"""
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Assign the code list to the evolving item.
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The code list is aligned with the evolving item's sub-tasks.
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If a task is not implemented, put a None in the list.
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"""
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raise NotImplementedError
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def evolve(
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self,
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*,
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evo: EvolvingItem,
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queried_knowledge: CoSTEERQueriedKnowledge | None = None,
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**kwargs,
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) -> EvolvingItem:
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# 1.找出需要evolve的task
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to_be_finished_task_index = []
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for index, target_task in enumerate(evo.sub_tasks):
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target_task_desc = target_task.get_task_information()
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if target_task_desc in queried_knowledge.success_task_to_knowledge_dict:
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evo.sub_workspace_list[index] = queried_knowledge.success_task_to_knowledge_dict[
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target_task_desc
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].implementation
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elif (
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target_task_desc not in queried_knowledge.success_task_to_knowledge_dict
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and target_task_desc not in queried_knowledge.failed_task_info_set
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):
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to_be_finished_task_index.append(index)
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# 2. 选择selection方法
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# if the number of factors to be implemented is larger than the limit, we need to select some of them
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if self.settings.select_threshold < len(to_be_finished_task_index):
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# Select a fixed number of factors if the total exceeds the threshold
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to_be_finished_task_index = self.select_one_round_tasks(
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to_be_finished_task_index, evo, self.settings.select_threshold, queried_knowledge, self.scen
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)
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result = multiprocessing_wrapper(
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[
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(
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self.implement_one_task,
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(evo.sub_tasks[target_index], queried_knowledge, evo.experiment_workspace),
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)
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for target_index in to_be_finished_task_index
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],
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n=RD_AGENT_SETTINGS.multi_proc_n,
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)
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code_list = [None for _ in range(len(evo.sub_tasks))]
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for index, target_index in enumerate(to_be_finished_task_index):
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code_list[target_index] = result[index]
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evo = self.assign_code_list_to_evo(code_list, evo)
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evo.corresponding_selection = to_be_finished_task_index
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# After implementation, the feedback should be reset
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for workspace in evo.sub_workspace_list:
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workspace.feedback = None
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return evo
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